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Exosomal Prognostic Score (EXPS)-based Prognostic Model in Pancreatic Tumours: Construction and Validation

ABSTRACT: Pancreatic adenocarcinoma (PAAD) is a highly lethal malignancy with limited therapeutic options and poor prognosis. Extracellular vesicles (EVs), essential mediators of intercellular communication, are emerging as non-invasive biomarkers for diagnosis and prognosis. Investigating exosomal gene expression offers a more accurate prognostic tool for PAAD patients.We analyzed four datasets from public databases: PAAD_ExoRbase, TcgaTargetGtex-PAAD, GSE62452_GPL6244, and GSE78229_GPL6244. Differentially expressed exosomal genes were identified, followed by univariate Cox regression to pinpoint prognosis-associated exogenes. Single-cell analysis and single-sample gene set enrichment analysis (ssGSEA) refined the gene selection, assessing their origins and predictive value for immune status. In vitro experiments and RNA-seq validated the regulatory role of the key gene KRT7.LASSO Cox regression identified five prognostic genes: KRT7, KRT19, CXCL5, TNFRSF13C, and GP1BA. The prognostic model accurately predicted overall survival in the TCGA-PAAD, GSE62452, and GSE78229 cohorts. Single-cell analysis revealed the gene signature originated from T-proliferative, malignant, and ductal cells. Immune cell infiltration and immune checkpoint analyses confirmed the model’s predictive strength. In vitro assays and RNA-seq demonstrated that KRT7 promotes PAAD cell proliferation, migration, and invasion.

ORGANISM(S): Mus musculus

PROVIDER: GSE283773 | GEO | 2025/12/05

REPOSITORIES: GEO

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Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

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Exosomal Prognostic Score (EXPS)-based Prognostic Model in Pancreatic Tumours: Construction and Validation

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